Spectroscopic evaluation of wastewater

ABSTRACT

A characteristic of wastewater may be evaluated using a spectrometer ( 110 ). For example, optical reflectance data may be obtained from wastewater, the reflectance data corresponding to a specified range of infra-red wavelengths. A value corresponding to the characteristic may be output based on the reflectance data generated by the spectrometer. The characteristic may include oil and grease or nitrogen as a percentage or concentration in a sample, for example.

CROSS-REFERENCE TO RELATED APPLICATIONS

This application claims the benefit of U.S. Provisional PatentApplication No. 63/060,782, filed 4 Aug. 2020, which is incorporatedherein by reference in its entirety.

BACKGROUND

Scientists employ a variety of analytical tools to assist inquantitative evaluation of various characteristics of products, from rawmaterials to finished goods. Generally, analytical tools may rely uponcareful control and preparation of a sample for evaluation, such asaccording to a standardized test or evaluation protocol in a “bench”setting. In this manner, traceable and repeatable results may beobtained. Such techniques may be applied to wastewater. Use ofanalytical techniques to evaluate wastewater helps to verify or maintainquality throughout the production and distribution process. For example,after processing, bench analytical techniques may be used to verify thatminor components are at or below specified levels. Analytical techniquesmay also be used to assess wastewater for a presence of contaminants oradulterants.

BRIEF DESCRIPTION OF THE DRAWINGS

In the drawings, which are not necessarily drawn to scale, like numeralsmay describe similar components in different views. Like numerals havingdifferent letter suffixes may represent different instances of similarcomponents. The drawings illustrate generally, by way of example, butnot by way of limitation, various embodiments discussed in the presentdocument.

FIG. 1 illustrates generally an example showing a system that mayinclude a spectrometer, such as for characterization of wastewater.

FIG. 2 illustrates generally an example comprising a technique, such asan automated method, for determining an indication of oil and grease ina wastewater sample using a spectrometer.

FIG. 3 illustrates generally an example comprising a technique, such asan automated method, for determining an indication of nitrogen in awastewater sample using a spectrometer.

FIG. 4 illustrates generally an example comprising a user input anddisplay, such as a touch-screen user interface, such as may be used toreceive inputs to control a spectrometer or to present results, such asa representation of a characteristic of wastewater being assessed usingthe spectrometer.

FIG. 5 illustrates generally an illustrative example ofexperimentally-obtained results including a predicted concentration ofoil and grease value obtained by transforming spectroscopic data,plotted versus corresponding measurements of the concentration of oiland grease value using a laboratory technique.

FIG. 6 illustrates generally an illustrative example ofexperimentally-obtained results including a predicted concentration ofnitrogen value obtained by transforming spectroscopic data, plottedversus corresponding measurements of the concentration of nitrogen valueusing a laboratory technique.

FIG. 7 illustrates a block diagram of an example comprising a machineupon which any one or more of the techniques (e.g., methodologies)discussed herein may be performed.

DETAILED DESCRIPTION

The systems and methods described herein use spectroscopy tocharacterize wastewater, such as using a reflectance spectroscopytechnique in a near-infrared range of wavelengths. These systems andmethods may include evaluating a sample to determine a percentage orconcentration of a particular characteristic. For example, thecharacteristic may include oil and grease or nitrogen. A value as apercentage or concentration may be output based on raw spectroscopy dataoutput from a spectrometer.

A sample of wastewater may be evaluated using the systems and methodsdescribed herein. The sample may be taken from wastewater, for examplefrom a tanker, hopper, transport container, etc., such as in-situ on atrain car, at a farm, at a distribution or processing facility, or thelike. The sample may be prepared, for example by melting, stirring,shaking, covering, placed in a clear container, such as a petri dish, orcooled (e.g., allowed to solidify), or a slurry cup (e.g., with aspecified thickness). A wastewater sample may be shaken, measured (e.g.,to around 6 g), and covered in a petri dish (e.g., not heated). Thewastewater may be blended, for example for 15-60 seconds (e.g., 30seconds). The prepared samples may be placed into a spectrometer.

The spectrometer may scan the prepared sample, for example with infraredspectroscopy. A processor (e.g., of a spectrometer device) may be usedto determine a value corresponding to a characteristic for the preparedsample based on a result of the scan (e.g., based on raw data output bythe spectrometer). The value corresponding to the characteristic may beoutput, for example displayed on a display device of the spectrometer,sent to a remote device (e.g., a mobile device such as a phone fordisplay), or the like. The process may be repeated (e.g., two to fourtimes), for example for the wastewater sample (e.g., with the sameprepared sample) to generate a value indicative of a central tendency,such as an average or median value. This may help avoid inconsistencies.

FIG. 1 illustrates generally an example showing a system 100 that mayinclude a spectrometer 110, such as for characterization wastewater,such as from a tank 108 or a tanker 102. Evaluation of a characteristicof the wastewater may be performed with the wastewater within a holdingvessel in an example. In another example, the characteristic may beevaluated without needing to remove a sample from the tank 108 or tanker102. The spectrometer 110 may include a user interface 130, such asincluding a user input or a display, as mentioned in relation to otherexamples described herein. In an example, the spectrometer 110 may beportable, such as sized and shaped to be manipulated by a user by hand.The spectrometer may be configured to emit light comprising a specifiedrange of infra-red wavelengths, and to receive a reflection fromwastewater. The spectrometer 110 may then establish reflectance datacorresponding to the received reflection without requiring physicalcontact between the spectrometer 110 and the wastewater.

The spectrometer 110 may include a processor circuit configured toprovide reflectance data comprising a series of values corresponding todiscrete wavelength values spanning a specified range of wavelengths. Asan illustrative example, the specified range may include wavelengthsfrom about 400 nanometers to about 2500 nanometers. The spectrometer 110may include a housing and hardware configuration similar to the FOSS NIR5000/6500 or DS2500 (available from Foss, Hilleroed, Denmark) or theSCiO apparatus (available from Consumer Physics, Tel Aviv, Israel).Reflectance data from a range of 750 nanometers to 1070 nanometers maybe provided. The use of reflectance spectroscopy in the near-infraredrange of wavelengths is illustrative, and other spectroscopic techniquesmay be used. The spectrometer 110 may be coupled via a wired or wirelesscommunication channel 120A to another device, such as a device 104(e.g., a mobile device such as a cellular handset, a tablet device, a“phablet” device having a cellular or wireless networking adaptor, alaptop or desktop computer, or a base-station located in a facilityhousing the flying apparatus 102, as illustrative examples).

The wireless communication channel 120A may be established according toa wireless communication standard such as Bluetooth® (e.g., Bluetooth®Low Energy (BLE) as described in the Bluetooth Core Specification, v.5.0, published Dec. 6, 2016, by the Bluetooth® Special Interest Group,Kirkland, Wash.) or according to one or more other standards (e.g., theInstitute of Electrical and Electronics Engineers (IEEE) 802.11 familyof standards known as Wi-Fi®, mobile communications standards such asrelating to 4G/Long Term Evolution (LTE), or the IEEE 802.15.4 family ofstandards, as illustrative examples).

The device 104 may include one or more processor circuits coupled to oneor more memory circuits. For example, the device 104 may be configuredto transform received reflectance data provided by the spectrometer 110such as using a model profile to generate a value of a characteristicbeing assessed. The device 104 may be coupled through another wirelesscommunication channel 122A to a repository 106 such as aremotely-located server or a cloud-based (e.g., distributed) facility.For example, the wireless communication channel 122A may be establishedaccording to a wireless networking protocol mentioned above, or adigital cellular networking protocol, as illustrative examples. One ormore criteria may be applied to the transformed reflectance data. Forexample, a value of a characteristic being assessed, such as a parameterrelating to a concentration of oil and grease or a concentration ofnitrogen, may be determined from spectroscopy data for the wastewater.The result may be presented to a user. The representation (which mayinclude a color code, such as green or red corresponding to a threshold)may be presented to the user via the user interface 130 of thespectrometer or the device 104, or the like.

In another example, the device 104 serves as an intermediary device, andthe repository 106 (or other facility such as a cloud-based resource)may perform the transformation of the reflectance data to establish avalue of the characteristic being assessed. In yet another example, thespectrometer 110 includes one or more processor circuits coupled to oneor more memory circuits, and the device 104 need not be used. Forexample, the spectrometer 110 may transmit reflectance data to therepository 106 for processing (e.g., transformation), or thespectrometer 110 may transform reflectance data.

Data generated by the spectrometer may be used to generate a percentageor concentration of a characteristic in a sample. Calibration models,for each product type and analyte as described herein, may be based onan array of data created from the NIR spectra points and the wetchemistry analysis values.

The NIR spectra, including the signature of the samples, is correlatedto the reference analysis values for specific analytes, for example awet chemistry analysis method. This creates an algorithm or calibrationmodel that may be used to predict the analyte values for similarproducts that fall within the parameter of the calibration model. TheNIR spectra points may be generally collected at every 0.5 nm, from 400to 2500 nm wavelength range, in an example.

To create the algorithm models, the step between points may be widenedand only part of the wavelength range may be used. In an example, themath treatments selected are: 1st or 2nd derivatives, Gap of 4 to 24,1st smoothing 4 to 24, 2nd smoothing 1 or 2

Scatter correction pre-processing may be done using standard normalvariate and detrending. An algorithm may be created, for example using aModified Partial Least Square (MPLS) method, for example based on aprocess initially defined by Shenk, J. S. and Westerhaus, M. O. (1991),Population Structuring of Near Infrared Spectra and Modified PartialLeast Squares Regression. Crop Sciences 31, pp. 1548-1555.

MPLS involves a process of removing multivariate outliers & ‘inliers’ ina 2-step process. It involves the computation of Mahalanobis distancesand in the 1st step data within the 3.0 boundary is selected and in a2nd step, the data points further than 0.6 from each other are selected.The calibration models may then be developed using dedicated software(e.g., WinISI from Foss Analytics of Denmark).

Similar results may be obtained different software & mathematics, suchas with calibrator software or from the many machine learning algorithmsor modelling framework available such as MATLAB, Unscrambler, R Earth,Python Py-Earth, Multivariate adaptive regression spline (MARS), or thelike.

FIG. 2 illustrates generally an example comprising a technique 900, suchas an automated method, for determining an indication of oil and greasein a wastewater sample using a spectrometer. The technique 900 includesan operation 902 to receive a prepared sample of wastewater at aspectrometer. The sample may be prepared by agitating or covering thewastewater, for example in a petri dish or a slurry cup (e.g., with aspecified thickness). The sample may be prepared directly from a mobiletanker or a bulk tanker in an example. The technique 900 may beperformed on-site where the sample is extracted.

The technique 900 includes an operation 904 to scan, using thespectrometer, the prepared sample with infrared spectroscopy. Theinfrared spectroscopy may include infrared transmission spectroscopy orinfrared reflection spectroscopy. A wavelength of the infraredspectroscopy may be within a near infrared spectrum, for example (e.g.,780 nm to 2500 nm). In an example, the wavelength may be within a rangeof frequencies between 400 nanometers and 2500 nanometers. Thespectrometer may be a portable or mobile spectrometer.

The technique 900 includes an operation 906 to determine, for exampleusing a processor (e.g., of the spectrometer), an indication of oil andgrease in the wastewater sample based on a result of the scan. Theindication may include a quantitative indication, such as a relativeindication, a ratio, a fraction such as a decimal fraction, aconcentration, or a percentage. Operation 906 may include converting rawspectrometer readings or data to a characteristic value using a formula.

The technique 900 includes an operation 908 to output the indication ofoil and grease in the wastewater sample. Operation 908 may includedisplaying the indication of oil and grease in the wastewater sample ona display of the spectrometer or sending the indication of oil andgrease in the wastewater sample to a mobile device for display. In anexample, operation 908 may include outputting an average or median oftwo or more iterations of the technique 900.

FIG. 3 illustrates generally an example comprising a technique 1000,such as an automated method, for determining an indication of nitrogenin a wastewater sample using a spectrometer. The technique 1000 includesan operation 1002 to receive a prepared sample of wastewater at aspectrometer. The sample may be prepared by agitating or covering thewastewater, for example in a petri dish or a slurry cup (e.g., with aspecified thickness). The sample may be prepared directly from a mobiletanker or a bulk tanker in an example. The technique 1000 may beperformed on-site where the sample is extracted.

The technique 1000 includes an operation 1004 to scan, using thespectrometer, the prepared sample with infrared spectroscopy. Theinfrared spectroscopy may include infrared transmission spectroscopy orinfrared reflection spectroscopy. A wavelength of the infraredspectroscopy may be within a near infrared spectrum, for example (e.g.,780 nm to 2500 nm). In an example, the wavelength may be within a rangeof frequencies between 400 nanometers and 2500 nanometers. Thespectrometer may be a portable or mobile spectrometer.

The technique 1000 includes an operation 1006 to determine, for exampleusing a processor (e.g., of the spectrometer), an indication of nitrogenin the wastewater sample based on a result of the scan. The indicationmay include a quantitative indication, such as a relative indication, aratio, a fraction such as a decimal fraction, a concentration, or apercentage. Operation 1006 may include converting raw spectrometerreadings or data to a characteristic value using a formula.

The technique 1000 includes an operation 1008 to output the indicationof nitrogen in the wastewater sample. Operation 1008 may includedisplaying the indication of nitrogen in the wastewater sample on adisplay of the spectrometer or sending the indication of nitrogen in thewastewater sample to a mobile device for display. In an example,operation 1008 may include outputting an average or median of two ormore iterations of the technique 1000.

FIG. 4 illustrates generally an example 1100 comprising a user input anddisplay, such as a touch-screen user interface 1130, such as may be usedto receive inputs to control a spectrometer or to present results, suchas a representation of a characteristic of wastewater being assessedusing the spectrometer (such as the spectrometer 110 shown in FIG. 1 ),or a separate device in communication with the spectrometer, such as amobile device or tablet. As an illustrative example, an input 1110 maybe used to receive an indication from the user that a particularcharacteristic is to be tested. Another input 1115 may be used toreceive an indication from the user that the spectrometer is to becalibrated. An input 1120 may used to receive an indication from theuser that a scan of a sample is to be initiated.

As mentioned in relation to various examples herein, data obtained usingthe spectrometer may be used to output a value of a characteristic beingassessed, such as oil and grease or nitrogen. The value itself may bepresented on a display 1150 of the touch-screen user interface 1130 or asimplified representation may be presented (e.g., a pass/fail indicationvia a light or lights, for example based on a threshold). For example,the simplified representation may include a visual indication that thesample (i.e., wastewater) has a value for the characteristic over orbelow a threshold or within a range, such as via a “traffic light”(green/yellow/red, for example below a first threshold green, within arange between thresholds yellow, and above a second threshold red) stylerepresentation having three indicators 1125A, 1125B, or 1125Crepresenting the threshold or range. Such states may be defined in avariety of manners, such as including a first state corresponding to“OK,” an second state such as “possibly unusable” or “try again,” or athird state indicative that the wastewater sample has a characteristicabove a threshold for example “not ok.”

The interface of the example 1100 of FIG. 4 shows user inputs unifiedwith a display for presentation of results, but these elements may alsobe separate. For example, the inputs may be provided by soft-keysaligned with a display, or by a separate keypad or input (e.g.,switches, knobs, etc.). The display may include a bit-field display orother display (e.g., an LED or liquid-crystal display having pre-defineddisplay elements, such as a numerical indicator 1140 havingseven-segment digits or other arrangement or indicators 1125A, 1125B,1125C comprising LED lamps). As an illustrative example, a unitlessscale may be shown, such as a simplified numerical scale having valuesfrom one to five, or one to ten, such as having higher values toindicate relative concentration or percentage of the characteristic inthe sample. Various aspects may be presented on the display 150, whichmay include a touchscreen display for receiving user input anddisplaying information.

Experimentally-Obtained Results

The results shown in FIG. 5 and FIG. 6 were obtained using a DS2500device with a prepared sample inserted within, as described herein. Ineach of FIG. 5 and FIG. 6 , the results in the graph show a predictedvalue (from a near-infrared spectroscopy result) against a wet chemistry(e.g., laboratory) derived value for a particular sample. A regressionline is shown, which fits within two control lines (e.g., thresholdlines of tolerance). A 45-degree line is shown for reference as well(e.g., a perfect fit). The majority of predicted results fit within thecontrol lines, and the regression line fits within the control lines. Inan example, the regression line may be determined using a ResidualPrediction Deviation (RPD), by taking a standard deviation ofcharacteristic values divided by a Root Mean Square Error or Prediction(RMSEP) value.

FIG. 5 illustrates generally an illustrative example ofexperimentally-obtained results including a predicted concentration ofoil and grease value obtained by transforming spectroscopic data,plotted versus corresponding measurements of the concentration of oiland grcasc value using a laboratory technique.

FIG. 6 illustrates generally an illustrative example ofexperimentally-obtained results including a predicted concentration ofnitrogen value obtained by transforming spectroscopic data, plottedversus corresponding measurements of the concentration of nitrogen valueusing a laboratory technique.

FIG. 7 illustrates a block diagram of an example comprising a machine2200 upon which any one or more of the techniques (e.g., methodologies)discussed herein may be performed. The machine 2200 may be included as aportion of elements shown in the system 100 of FIG. 1 . In variousexamples, the machine 2200 may operate as a standalone device or may beconnected (e.g., networked) to other machines. In a networkeddeployment, the machine 2200 may operate in the capacity of a servermachine, a client machine, or both in server-client networkenvironments. In an example, the machine 2200 may act as a peer machinein peer-to-peer (P2P) (or other distributed) network environment. Themachine 2200 may be a personal computer (PC), a tablet device, apersonal digital assistant (PDA), a mobile telephone, a web appliance, anetwork router, switch or bridge, a portable (e.g., hand-held)spectrometer such as including a microprocessor or microcontroller, orany machine capable of executing instructions (sequential or otherwise)that specify actions to be taken by that machine. Further, while only asingle machine is illustrated, the term “machine” shall also be taken toinclude any collection of machines that individually or jointly executea set (or multiple sets) of instructions to perform any one or more ofthe methodologies discussed herein, such as cloud computing, software asa service (SaaS), other computer cluster configurations.

Examples, as described herein, may include, or may operate by, logic ora number of components, or mechanisms. “Circuitry” refers generally acollection of circuits implemented in tangible entities that includehardware (e.g., simple circuits, gates, logic elements, etc.). Circuitrymembership may be flexible over time and underlying hardwarevariability. Circuitries include members that may, alone or incombination, perform specified operations when operating. In an example,hardware of the circuitry may be immutably designed to carry out aspecific operation (e.g., hardwired). In an example, the hardwarecomprising the circuitry may include variably connected physicalcomponents (e.g., execution units, transistors, simple circuits, etc.)including a computer readable medium physically modified (e.g.,magnetically, electrically, such as via a change in physical state ortransformation of another physical characteristic, etc.) to encodeinstructions of the specific operation.

In connecting the physical components, the underlying electricalproperties of a hardware constituent may be changed, for example, froman insulating characteristic to a conductive characteristic or viceversa. The instructions enable embedded hardware (e.g., the executionunits or a loading mechanism) to create members of the circuitry inhardware via the variable connections to carry out portions of thespecific operation when in operation. Accordingly, the computer readablemedium is communicatively coupled to the other components of thecircuitry when the device is operating. In an example, any of thephysical components may be used in more than one member of more than onecircuitry. For example, under operation, execution units may be used ina first circuit of a first circuitry at one point in time and reused bya second circuit in the first circuitry, or by a third circuit in asecond circuitry at a different time.

Machine (e.g., computer system) 2200 may include a hardware processor2202 (e.g., a central processing unit (CPU), a graphics processing unit(GPU), a hardware processor core, or any combination thereof), a mainmemory 2204 and a static memory 2206, some or all of which maycommunicate with each other via an interlink (e.g., bus) 2208. Themachine 2200 may further include a display unit 2210, an alphanumericinput device 2212 (e.g., a keyboard), and a user interface (UI)navigation device 2214 (e.g., a mouse). In an example, the display unit2210, input device 2212 and UI navigation device 2214 may be a touchscreen display. The machine 2200 may additionally include a storagedevice (e.g., drive unit) 2216, a signal generation device 2218 (e.g., aspeaker), a network interface device 2220, and one or more sensors 2221,such as a global positioning system (GPS) sensor, compass,accelerometer, or other sensor. The machine 2200 may include an outputcontroller 2228, such as a serial (e.g., universal serial bus (USB),parallel, or other wired or wireless (e.g., infrared (IR), near fieldcommunication (NFC), etc.) connection to communicate or control one ormore peripheral devices (e.g., a printer, card reader, etc.).

The storage device 2216 may include a machine readable medium 2222 onwhich is stored one or more sets of data structures or instructions 2224(e.g., software) embodying or utilized by any one or more of thetechniques or functions described herein. The instructions 2224 may alsoreside, completely or at least partially, within the main memory 2204,within static memory 2206, or within the hardware processor 2202 duringexecution thereof by the machine 2200. In an example, one or anycombination of the hardware processor 2202, the main memory 2204, thestatic memory 2206, or the storage device 2216 may constitute machinereadable media.

While the machine readable medium 2222 is illustrated as a singlemedium, the term “machine readable medium” may include a single mediumor multiple media (e.g., a centralized or distributed database, and/orassociated caches and servers) configured to store the one or moreinstructions 2224.

The term “machine readable medium” may include any medium that iscapable of storing, encoding, or carrying instructions for execution bythe machine 2200 and that cause the machine 2200 to perform any one ormore of the techniques of the present disclosure, or that is capable ofstoring, encoding or carrying data structures used by or associated withsuch instructions. Non-limiting machine readable medium examples mayinclude solid-state memories, and optical and magnetic media.Accordingly, machine-readable media are not transitory propagatingsignals. Specific examples of massed machine readable media may include:non-volatile memory, such as semiconductor memory devices (e.g.,Electrically Programmable Read-Only Memory (EPROM), ElectricallyErasable Programmable Read-Only Memory (EEPROM)) and flash memorydevices; magnetic or other phase-change or state-change memory circuits;magnetic disks, such as internal hard disks and removable disks;magneto-optical disks; and CD-ROM and DVD-ROM disks.

The instructions 2224 may further be transmitted or received over acommunications network 2226 using a transmission medium via the networkinterface device 2220 utilizing any one of a number of transferprotocols (e.g., frame relay, internet protocol (IP), transmissioncontrol protocol (TCP), user datagram protocol (UDP), hypertext transferprotocol (HTTP), etc.). Example communication networks may include alocal area network (LAN), a wide area network (WAN), a packet datanetwork (e.g., the Internet), mobile telephone networks (e.g., cellularnetworks such as conforming to one or more standards such as a 4Gstandard or Long Term Evolution (LTE)), Plain Old Telephone (POTS)networks, and wireless data networks (e.g., Institute of Electrical andElectronics Engineers (IEEE) 802.11 family of standards known as Wi-Fi®,IEEE 802.15.4 family of standards, peer-to-peer (P2P) networks, amongothers. In an example, the network interface device 2220 may include oneor more physical jacks (e.g., Ethernet, coaxial, or phone jacks) or oneor more antennas to connect to the communications network 2226. In anexample, the network interface device 2220 may include a plurality ofantennas to wirelessly communicate using at least one of single-inputmultiple-output (SIMO), multiple-input multiple-output (MIMO), ormultiple-input single-output (MISO) techniques. The term “transmissionmedium” shall be taken to include any intangible medium that is capableof storing, encoding or carrying instructions for execution by themachine 2200, and includes digital or analog communications signals orother intangible medium to facilitate communication of such software.

Each of the non-limiting examples below can stand on its own, or can becombined in various permutations or combinations with one or more of theother aspects or other subject matter described in this document.

Example 1 is a method for near infrared evaluation of a characteristicof a sample, the method comprising: receiving a prepared sample of thesample at a spectrometer; scanning, using the spectrometer, the preparedsample with infrared spectroscopy; determining, using a processor, avalue corresponding to the characteristic for the sample based on aresult of the scan; and outputting the value.

In Example 2, the subject matter of Example 1 includes, wherein theprepared sample is prepared by covering the sample in a petri dish or aslurry cup.

In Example 3, the subject matter of Examples 1-2 includes, wherein theinfrared spectroscopy includes infrared transmission spectroscopy orinfrared reflection spectroscopy.

In Example 4, the subject matter of Examples 1-3 includes, wherein awavelength of the infrared spectroscopy is within a near infraredspectrum.

In Example 5, the subject matter of Examples 1˜4 includes, wherein awavelength of the infrared spectroscopy is within a range of frequenciesbetween 400 nanometers and 2500 nanometers.

In Example 6, the subject matter of Examples 1-5 includes, wherein thesample is prepared directly from a mobile tanker or a bulk tanker andwherein the method is performed on-site where the sample is extracted.

In Example 7, the subject matter of Examples 1-6 includes, wherein thespectrometer is a portable spectrometer.

In Example 8, the subject matter of Examples 1-7 includes, whereinoutputting the value includes displaying the value on a display of thespectrometer or sending the value to a mobile device for display.

In Example 9, the subject matter of Examples 1-8 includes, wherein theprepared sample is prepared by agitating the sample while melting thesample (if necessary).

In Example 10, the subject matter of Examples 1-9 includes, whereinoutputting the value includes outputting an average or median of two ormore iterations of the method.

In Example 11, the subject matter of Examples 1-10 includes, wherein thesample is a wastewater sample.

In Example 12, the subject matter of Examples 1-11 includes, wherein thevalue corresponding to the characteristic for the sample includes aconcentration of oil and grease in the sample or a concentration ofnitrogen in the sample.

Example 13 is a system for near infrared evaluation of a characteristicof a sample, the system comprising: a spectrometer configured to: emitlight comprising a specified range of infrared wavelengths; receive areflection from a prepared sample of the sample; and establishreflectance data corresponding to the received reflection; and aprocessor circuit coupled to a memory circuit and communicativelycoupled to the spectrometer, the processor circuit configured to:determine a value corresponding to the characteristic for the samplebased on a result of the scan; and output the value.

In Example 14, the subject matter of Example 13 includes, wherein theprepared sample is prepared by covering the sample in a petri dish or aslurry cup.

In Example 15, the subject matter of Examples 13-14 includes, whereinthe infrared spectroscopy includes infrared transmission spectroscopy orinfrared reflection spectroscopy.

In Example 16, the subject matter of Examples 13-15 includes, wherein awavelength of the infrared spectroscopy is within a near infraredspectrum.

In Example 17, the subject matter of Examples 13-16 includes, wherein awavelength of the infrared spectroscopy is within a range of frequenciesbetween 400 nanometers and 2500 nanometers.

In Example 18, the subject matter of Examples 13-17 includes, whereinthe sample is prepared directly from a mobile tanker or a bulk tankerand wherein the method is performed on-site where the sample isextracted.

In Example 19, the subject matter of Examples 13-18 includes, whereinthe spectrometer is a portable spectrometer.

In Example 20, the subject matter of Examples 13-19 includes, whereinthe spectrometer includes a display, and wherein to output the value,the processor circuit is configured to cause the display to present thevalue.

In Example 21, the subject matter of Examples 13-20 includes, whereinthe prepared sample is prepared by agitating the sample while meltingthe sample (if necessary).

In Example 22, the subject matter of Examples 13-21 includes, wherein tooutput the value, the processor circuit is configured to determine asecond value and output an average of the value and the second value.

In Example 23, the subject matter of Examples 13-22 includes, whereinthe sample is a wastewater sample.

In Example 24, the subject matter of Examples 13-23 includes, whereinthe value corresponding to the characteristic for the sample includes aconcentration of oil and grease in the sample or a concentration ofnitrogen in the sample.

Example 160 is a system for near infrared evaluation of a characteristicof an wastewater sample, the system comprising: a spectrometerconfigured to: emit light comprising a specified range of infraredwavelengths; receive a reflection from a prepared sample of thewastewater sample; and establish reflectance data corresponding to thereceived reflection; and a processor circuit coupled to a memory circuitand communicatively coupled to the spectrometer, the processor circuitconfigured to: determine a percent of concentration of oil and grease inthe wastewater sample corresponding to the characteristic for thewastewater sample based on a result of the scan; and output the percentof concentration of oil and grease in the wastewater sample.

In Example 161, the subject matter of Example 160 includes, wherein theprepared sample is prepared by covering the wastewater sample in a petridish or a slurry cup.

In Example 162, the subject matter of Examples 160-161 includes, whereinthe infrared spectroscopy includes infrared transmission spectroscopy orinfrared reflection spectroscopy.

In Example 163, the subject matter of Examples 160-162 includes, whereina wavelength of the infrared spectroscopy is within a near infraredspectrum.

In Example 164, the subject matter of Examples 160-163 includes, whereina wavelength of the infrared spectroscopy is within a range offrequencies between 400 nanometers and 2500 nanometers.

In Example 165, the subject matter of Examples 160-164 includes, whereinthe wastewater sample is prepared directly from a mobile tanker or abulk tanker and wherein the method is performed on-site where thewastewater sample is extracted.

In Example 166, the subject matter of Examples 160-165 includes, whereinthe spectrometer is a portable spectrometer.

In Example 167, the subject matter of Examples 160-166 includes, whereinthe spectrometer includes a display, and wherein to output the percentof concentration of oil and grease in the wastewater sample, theprocessor circuit is configured to cause the display to present thepercent of concentration of oil and grease in the wastewater sample.

In Example 168, the subject matter of Examples 160-167 includes, whereinoutputting the value includes outputting an average or median of two ormore iterations of the method.

Example 169 is a method for near infrared evaluation of a characteristicof a wastewater sample, the method comprising: receiving a preparedsample of the wastewater sample at a spectrometer; scanning, using thespectrometer, the prepared sample with infrared spectroscopy;determining, using a processor, a percent of concentration of nitrogenin the wastewater sample based on a result of the scan; and outputtingthe percent of concentration of nitrogen in the wastewater sample.

In Example 170, the subject matter of Example 169 includes, wherein theprepared sample is prepared by covering the wastewater sample in a petridish or a slurry cup.

In Example 171, the subject matter of Examples 169-170 includes, whereinthe infrared spectroscopy includes infrared transmission spectroscopy orinfrared reflection spectroscopy.

In Example 172, the subject matter of Examples 169-171 includes, whereina wavelength of the infrared spectroscopy is within a near infraredspectrum.

In Example 173, the subject matter of Examples 169-172 includes, whereina wavelength of the infrared spectroscopy is within a range offrequencies between 400 nanometers and 2500 nanometers.

In Example 174, the subject matter of Examples 169-173 includes, whereinthe wastewater sample is prepared directly from a mobile tanker or abulk tanker and wherein the method is performed on-site where thewastewater sample is extracted.

In Example 175, the subject matter of Examples 169-174 includes, whereinthe spectrometer is a portable spectrometer.

In Example 176, the subject matter of Examples 169-175 includes, whereinoutputting the percent of concentration of nitrogen in the wastewatersample includes displaying the percent of concentration of nitrogen inthe wastewater sample on a display of the spectrometer or sending thepercent of concentration of nitrogen in the wastewater sample to amobile device for display.

In Example 177, the subject matter of Examples 169-176 includes, whereinoutputting the value includes outputting an average or median of two ormore iterations of the method.

Example 178 is a system for near infrared evaluation of a characteristicof an wastewater sample, the system comprising: a spectrometerconfigured to: emit light comprising a specified range of infraredwavelengths; receive a reflection from a prepared sample of thewastewater sample; and establish reflectance data corresponding to thereceived reflection; and a processor circuit coupled to a memory circuitand communicatively coupled to the spectrometer, the processor circuitconfigured to: determine a percent of concentration of nitrogen in thewastewater sample corresponding to the characteristic for the wastewatersample based on a result of the scan; and output the percent ofconcentration of nitrogen in the wastewater sample.

In Example 179, the subject matter of Example 178 includes, wherein theprepared sample is prepared by covering the wastewater sample in a petridish or a slurry cup.

In Example 180, the subject matter of Examples 178-179 includes, whereinthe infrared spectroscopy includes infrared transmission spectroscopy orinfrared reflection spectroscopy.

In Example 181, the subject matter of Examples 178-180 includes, whereina wavelength of the infrared spectroscopy is within a near infraredspectrum.

In Example 182, the subject matter of Examples 178-181 includes, whereina wavelength of the infrared spectroscopy is within a range offrequencies between 400 nanometers and 2500 nanometers.

In Example 183, the subject matter of Examples 178-182 includes, whereinthe wastewater sample is prepared directly from a mobile tanker or abulk tanker and wherein the method is performed on-site where thewastewater sample is extracted.

In Example 184, the subject matter of Examples 178-183 includes, whereinthe spectrometer is a portable spectrometer.

In Example 185, the subject matter of Examples 178-184 includes, whereinthe spectrometer includes a display, and wherein to output the percentof concentration of nitrogen in the wastewater sample, the processorcircuit is configured to cause the display to present the percent ofconcentration of nitrogen in the wastewater sample.

In Example 186, the subject matter of Examples 178-185 includes, whereinoutputting the value includes outputting an average or median of two ormore iterations of the method.

Example 187 is at least one machine-readable medium includinginstructions that, when executed by processing circuitry, cause theprocessing circuitry to perform operations to implement of any ofExamples 1-186.

Example 188 is an apparatus comprising means to implement of any ofExamples 1-186.

Example 189 is a system to implement of any of Examples 1-186.

Example 190 is a method to implement of any of Examples 1-186.

Method examples described herein can be machine or computer-implementedat least in part. Some examples can include a computer-readable mediumor machine-readable medium encoded with instructions operable toconfigure an electronic device to perform methods as described in theabove examples. An implementation of such methods can include code, suchas microcode, assembly language code, a higher-level language code, orthe like. Such code can include computer readable instructions forperforming various methods. The code may form portions of computerprogram products. Further, in an example, the code can be tangiblystored on one or more volatile, non-transitory, or non-volatile tangiblecomputer-readable media, such as during execution or at other times.Examples of these tangible computer-readable media can include, but arenot limited to, hard disks, removable magnetic disks, removable opticaldisks (e.g., compact disks and digital video disks), magnetic cassettes,memory cards or sticks, random access memories (RAMs), read onlymemories (ROMs), and the like.

1. A method for near infrared evaluation of a characteristic of a wastewater sample, the method comprising: receiving a prepared sample of the wastewater sample at a spectrometer; scanning, using the spectrometer, the prepared sample with infrared spectroscopy; determining, using a processor, a characteristic in the wastewater sample based on a result of the scan; and outputting the characteristic in the wastewater sample.
 2. The method of claim 1, wherein the prepared sample is prepared by covering the wastewater sample in a petri dish or a slurry cup.
 3. The method of claim 1, wherein the infrared spectroscopy includes infrared transmission spectroscopy or infrared reflection spectroscopy.
 4. The method of claim 1, wherein a wavelength of the infrared spectroscopy is within a near infrared spectrum.
 5. The method of claim 1, wherein a wavelength of the infrared spectroscopy is within a range of frequencies between 400 nanometers and 2500 nanometers.
 6. The method of claim 1, wherein the wastewater sample is prepared directly from a mobile tanker or a bulk tanker and wherein the method is performed on-site where the wastewater sample is extracted.
 7. The method of claim 1, wherein the spectrometer is a portable spectrometer.
 8. The method of claim 1, wherein outputting the characteristic in the wastewater sample includes displaying the characteristic in the wastewater sample on a display of the spectrometer or sending the characteristic in the wastewater sample to a mobile device for display.
 9. The method of claim 1, wherein outputting the value includes outputting an average or median of two or more iterations of the method.
 10. The method of claim 1, wherein the characteristic in the wastewater sample is a percent of concentration of oil and grease.
 11. The method of claim 1, wherein the characteristic in the wastewater sample is a percent of concentration of nitrogen.
 12. A system for near infrared evaluation of a characteristic of a wastewater sample, the system comprising: a spectrometer configured to: emit light comprising a specified range of infrared wavelengths; receive a reflection from a prepared sample of the wastewater sample; and establish reflectance data corresponding to the received reflection; and a processor circuit coupled to a memory circuit and communicatively coupled to the spectrometer, the processor circuit configured to: determine a characteristic in the wastewater sample corresponding to the characteristic for the wastewater sample based on a result of the scan; and output the characteristic in the wastewater sample.
 13. The system of claim 12, wherein the characteristic in the wastewater sample is a percent of concentration of oil and grease or a percent of concentration of nitrogen. 